Download Sensitivity of Prefrontal Cortex to Changes in Target Probability: A

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Human Brain Mapping 13:26 –33(2001)
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Sensitivity of Prefrontal Cortex to Changes in
Target Probability: A Functional MRI Study
B. J. Casey1*, Steven D. Forman2, Peter Franzen2, Aaron Berkowitz2,
Todd S. Braver3, Leigh E. Nystrom4, Kathleen M. Thomas1, and
Douglas C. Noll5
1
Sackler Institute for Developmental Psychobiology, Weill Medical College of Cornell University,
New York, New York
2
University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
3
Washington University, St. Louis, Missouri
4
Princeton University, Princeton, New Jersey
5
University of Michigan, Ann Arbor, Michigan
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Abstract: Electrophysiological studies suggest sensitivity of the prefrontal cortex to changes in the
probability of an event. The purpose of this study was to determine if subregions of the prefrontal cortex
respond differentially to changes in target probabilities using functional magnetic resonance imaging
(fMRI). Ten right-handed adults were scanned using a gradient-echo, echo planar imaging sequence
during performance of an oddball paradigm. Subjects were instructed to respond to any letter but “X”. The
frequency of targets (i.e., any letter but X) varied across trials. The results showed that dorsal prefrontal
regions were active during infrequent events and ventral prefrontal regions were active during frequent
events. Further, we observed an inverse relation between the dorsal and ventral prefrontal regions such
that when activity in dorsal prefrontal regions increased, activity in ventral prefrontal regions decreased,
and vice versa. This finding may index competing cognitive processes or capacity limitations. Most
importantly, these findings taken as a whole suggest that any simple theory of prefrontal cortex function
must take into account the sensitivity of this region to changes in target probability. Hum. Brain Mapping
13:26 –33, 2001. © 2001 Wiley-Liss, Inc.
Key words: prefrontal cortex; attention, inhibition; neuroimaging; fMRI
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INTRODUCTION
Electrophysiological studies suggest the sensitivity
of frontal regions to changes in the frequency or probContract grant sponsor: NIMH.
*Correspondence to: B. J. Casey, Ph.D., Sackler Institute for Developmental Psychobiology, Weill Medical College of Cornell University, 1300 York Avenue, Box 140, New York, NY 10021.
E-mail: [email protected]
Received for publication 12 November 1999; accepted 18 December
2000
©
2001 Wiley-Liss, Inc.
ability of an event [Sutton et al., 1965; Donchin and
Coles, 1985; McCarthy et al., 1997]. This association
has been argued most extensively in the context of the
P300, an event-related potential (ERP) elicited by infrequent events. Neuroimaging studies [Grasby et al.,
1993; Jonides et al., 1993; Cohen et al., 1994; McCarthy
et al., 1994; Casey et al., 1995; Smith et al., 1995; Kawashima et al., 1996; Casey et al., 1997] together with
physiological studies in monkeys [Niki, 1974; Mishkin
and Manning, 1978; Goldman-Rakic, 1987; Fuster,
1988; Yajeya and Fuster, 1988] have largely focused on
the involvement of frontal regions in higher cognitive
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Target Frequency and Prefrontal Cortex 䉬
processes such as working memory and inhibitory
control. These studies provide better spatial resolution
than do electrophysiology studies and suggest that
different regions within prefrontal cortex are associated with different types of processing. For example,
dorsolateral prefrontal cortex has been implicated in
working memory [Goldman-Rakic, 1987; Fuster, 1988;
Cohen et al., 1994; McCarthy et al., 1994; Smith et al.,
1995], while more ventral regions of prefrontal cortex
have been implicated in the suppression of prepotent
responses as in the go/no-go task [Kawashima et al.,
1996; Casey et al., 1997; Konishi et al., 1999]. Few
imaging studies have examined the effects of manipulating target probability on these presumed prefrontal functions. One example of such an attempt is a
recent study by McCarthy and colleagues [1997]. They
showed that increased dorsolateral prefrontal activity
was associated with infrequent events in an oddball
paradigm using both electrophysiology and fMRI. The
current study examined prefrontal activity as a function of changing target frequency (i.e., frequent and
infrequent) in a modified version of an oddball paradigm.
The goal of this study was to determine if regions of
prefrontal cortex differentially respond to changes in
the probability of an event. Previously, we have examined prefrontal activity to frequent targets in a
go/no-go version of the oddball paradigm [Casey et
al., 1997]. In that study, either all trials within a block
were targets (100%) or half the trials in a block were
targets (50%). Thus target frequency did not vary from
high to low but was constant within blocks of trials
(i.e., either 50% or 100%). In the current study we
manipulated the frequency of targets across trials allowing for high, moderate, and low frequencies of a
target to occur throughout the experiment. We assumed that this manipulation would allow us to determine the relevance of target frequency to prefrontal
function within a single task design.
Specifically, we assumed that rare targets would
increase interference in the stimulus demands of the
task and hypothesized that this would be associated
with increased activity in dorsolateral prefrontal regions, but not ventral prefrontal regions. Rare target
frequencies result in an increase in the number of
nontarget relative to target stimuli. By frequently presenting the nontarget, “X”, the representation of this
stimulus should become highly salient, relative to the
infrequent presentation of targets. We enhanced the
extent of interference between targets and nontargets
by having the targets consist of a large set of stimuli
(i.e., A–Z, except X) occurring infrequently and having
the nontarget be a single salient stimulus, the letter
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“X”. Increasing the salience of the nontarget (X)
should result in increased interference between it and
the set of target stimuli (A–Z, except X). Accordingly,
we hypothesized that this type of interference (i.e.,
among stimuli) would result in increased activity in
the dorsolateral prefrontal cortex. This hypothesis was
based on the assumption that dorsolateral prefrontal
cortex supports relevant stimulus information (e.g.,
any letter but X) against interference from competing
sources over time [Goldman-Rakic, 1987; Cohen and
Servan-Schreiber, 1992] and work by McCarthy et al.
[1997] associating dorsolateral prefrontal cortex activity with infrequent targets.
Conversely, we assumed that frequent targets
would increase interference in the response demands
of the task (i.e., when to respond or not) and hypothesized that this would activate ventral regions of prefrontal cortex but not dorsal prefrontal regions. Frequent targets result in an increase in the number of
responses relative to nonresponses. When a response
is required frequently to a target, the representation of
that response is activated repeatedly, making the response more salient. The salience of the response interferes with the task demands of not responding to an
X. If a response is required infrequently, as with rare
targets, the representation of that response is not activated repeatedly and therefore does not compete or
interfere with the task demands of not responding to
an X. Accordingly, we hypothesized that this type of
interference (between responses) would result in increased activity in the ventral prefrontal cortex. This
prediction was based in part on the presumed role of
ventral prefrontal cortex in suppression of prepotent
responses as in the go/no-go task [Kawashima et al.,
1996; Casey et al., 1997; Konishi et al., 1999].
MATERIALS AND METHODS
Subjects
Ten right-handed adult subjects (18 – 41 years old)
were recruited from the Pittsburgh area and were paid
$50 for their participation in the study. Data from one
male subject was excluded due to excessive in-plane
head motion of more than .5 voxels.
Behavioral task
Subjects were presented with a sequence of single
letters, one at a time, at the center of the visual display
and were instructed to respond to any letter except X.
The stimuli were presented for 300 ms with an interstimulus interval of 700 ms (refer to Fig. 1). Target
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Casey et al. 䉬
that mimics the scanner in appearance and sound.
Images were acquired on a 1.5T GE scanner modified
by Advanced NMR (Wilmington, MA) and a head
coil. A set of T1-weighted sagittal images was acquired using a spoiled gradient sequence (spin echo,
TE 18, TR 500) for localization and prescription of
coronal slices. A second set of T1-weighted coronal
images (spin echo, TE 18, TR 500, 4-mm skip 0) was
acquired covering the whole brain. Next, five sets of
T2*-weighted coronal images (4-mm skip 0) were acquired using an echo planar imaging (EPI) gradient
echo sequence (EPI-ISGR) with TE 40, TR 5000, and a
flip angle of 90°. Each of five sets of images consisted
of 52 repetitions of 23 slice locations (Fig. 2).
Figure 1.
An illustration of the behavioral task with the stimulus parameters
and timing of events.
Image analysis
probability varied between 10% and 60% in 120-sec
oscillations. Each subject performed five runs of two
oscillations and each run lasted 240 sec (i.e., 240 trials).
Stimulus presentation was controlled by a Macintosh
computer using Psyscope [Cohen et al., 1993], a rear
projection screen, and an Infocus projector system.
Subjects responded by pressing a designated button
on a specially constructed handheld fiber-optic response box, connected to a transducer via fiber optic
cables to the Macintosh. Both reaction times and accuracy were collected.
Each subject’s images were realigned in 3D space
using Wood’s automated image registration (AIR) algorithm [Woods et al., 1992]. All subjects’ data were
then registered to one representative subject’s brain
and pooled. Each 5-sec scan consisted of five 1-sec
behavioral trials. Scans were divided into those with
high (e.g., ⬎ two targets out of five trials), moderate
(two targets out of five trials), and low (e.g., ⬍ two
targets out of five trials) target probabilities. The first
two 5-sec scans of each of five runs were excluded
from the analysis to allow the hemodynamic response
to peak. Further we collected two 5 sec scans at the
end of each run to assess the signal change for the last
10 trials (i.e., 52 repetitions per run). Thus we analyzed
80 scans per probability condition (high, moderate,
and low), and the mean percentage of targets in each
Scanning procedures
All subjects were screened carefully for any metal
implants or contraindications for a MRI and then acclimated to the scanner environment in a simulator
Figure 2.
Sagittal view of acquired slice locations.
Bold lines indicate the slice locations
used for the analysis of the current
study.
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Target Frequency and Prefrontal Cortex 䉬
probability condition was approximately 60%, 30%,
and 10%, respectively. A 9 (subjects) ⫻ 3 (high, moderate, and low target probabilities) ANOVA was performed on the pooled data with subject as a random
factor, and areas of significant activation were identified that satisfied a contiguity threshold of three contiguous pixels with P ⬍ .005 [Forman et al., 1995]. Post
hoc t-tests were performed on the condition (high,
moderate, and low target probabilities) means to determine the direction of change in MR signal intensity.
Images were warped into stereotaxic space using
AFNI [Cox, 1996]. The time series for significant regions of activation were calculated by averaging
across subjects (9) and runs (5). For the purpose of this
study, activity was examined in the prefrontal cortex
from the genu of the corpus callosum to the frontal
pole to exclude motor areas in the analysis (refer to
Fig. 2). This area represents prefrontal regions of 17
mm ⬍ Y ⬍ 55 mm in Talairach stereotaxic space.
Localization of activity to specific gyri (i.e., superior,
middle, inferior, and orbital) in the prefrontal cortex
was based on the coordinate system of the Talairach
atlas [Talairach and Tournoux, 1988] and confirmed
by two independent raters using a standard brain atlas
[Duvernoy, 1991]. Inter-rater reliability was .98. The
results are presented by individual gyri and by region
(i.e., ventral and dorsal prefrontal cortex). Ventral prefrontal activation was defined as activity of the inferior frontal gyrus and/or orbital frontal gyrus and
dorsal prefrontal activation was defined as activity of
the superior frontal gyrus and/or middle frontal gyrus. The conditional means of MR signal intensity for
each significant cluster (identifed by gyrus) was averaged within the dorsal prefrontal region and likewise
within the ventral prefrontal region. Percent change in
MR signal intensity for each cluster was calculated as
the difference of the condition mean (high, moderate,
or low target probabilities) from the overall mean and
then divided by the overall mean. These percent differences were then averaged across clusters falling in
dorsal or ventral prefrontal regions.
Figure 3.
Percent change in MR signal as a function of high, moderate, and
low target frequency in ventral and dorsal prefrontal cortex (PFC).
results are presented in Figures 3 and 4 and Table I.
Seven clusters of significant activation were identified.
Each cluster is identified by location (e.g., gyrus), maximum F ratio, size in voxels, and percent change in
signal in Table I. As predicted, dorsal prefrontal activity increased when the target frequency was low and
ventral prefrontal activity increased when target frequency was high. There was an inverse relation between dorsal and ventral prefrontal activity whereby
when activity in dorsolateral prefrontal cortex increased, activity in ventral prefrontal cortex decreased
and vice versa. This pattern of results is depicted in
Figure 3. To illustrate that the change in MR signal
intensity corresponded to the experimental manipulation, the time course for the change in MR signal as a
function of target frequency is depicted in Figure 4 for
all seven clusters separately.
RESULTS
DISCUSSION
Behaviorally, mean accuracy rate and mean reaction
time did not differ significantly for rare and frequent
target probabilities. Mean accuracy rate across the entire experiment was 92% or greater.1 The imaging
The overall goal of this study was to determine if
regions of prefrontal cortex differentially respond to
changes in target probability. Specifically, we assumed that rare targets would increase interference in
the stimulus demands of the task and that this would
be associated with increased activity in dorsolateral
prefrontal regions. Conversely, we assumed that frequent targets would increase interference in the re-
1
The mean reaction times and mean accuracies across subjects for
the high, middle, and low target probability conditions were 427,
449, and 456 msec and 96%, 94%, and 93%, respectively.
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Casey et al. 䉬
Figure 4.
Time course of change in MR signal intensity averaged across subjects (n) as a function of target
frequency for (a) dorsal prefrontal cortex and (b) ventral prefrontal cortex. MR signal change is
plotted in black and target probability is plotted in red. [Color figure can be viewed in the online
issue, which is available at www.interscience.wiley.com.]
ther, in our previous study, an increase in number of
false alarms (i.e., more motor responses) was related
to a decrease in overall ventral prefrontal activity,
suggesting that those subjects who performed worse
and made extra motor responses activated ventral
prefrontal cortex less. Finally, in an event-related fMRI
study using a go/no-go task, Konishi et al. [1999]
showed ventral prefrontal activity during the no-go
trials. Taken together, our current results and previous
findings are consistent with the idea that the ventral
prefrontal cortex is involved in successful representation of the response demands of the task. Alternatively, the results may be interpreted as the ventral
prefrontal region being recruited simply to inhibit a
response [Diamond, 1990; Fuster, 1997; Roberts et al.,
1998]. If this were the case, one would expect this
region to be activated during the low target probability when a number of responses are being inhibited,
sponse demands of the task (i.e., when to respond)
and would activate ventral regions of prefrontal cortex. Our results appear to be consistent with our predictions in that rare targets were associated with dorsolateral prefrontal cortex activity and frequent targets
were associated with activity in ventral regions of
prefrontal cortex.
The most robust activation was observed during
frequent targets in ventral prefrontal cortex. One confound of the current task design is the greater number
of responses in the frequent versus rare target conditions. Accordingly, one may interpret the observed
ventral prefrontal activity as merely reflecting the increase in number of motor responses. However, our
previous study using a similar paradigm controlled
for the number of motor responses (by slowing the
stimulus presentation rate) and the same ventral prefrontal areas were activated [Casey et al., 1997]. Fur-
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Target Frequency and Prefrontal Cortex 䉬
TABLE I. Location and magnitude of prefrontal activation during performance of the go no-go task
Talairach
Brodmann’s
area
X
Y
Z
Dorsal Prefrontal Regions
R. Superior Frontal Gyrus
L. Superior Frontal Gyrus
R. Middle Frontal Gyrus
L. Middle Frontal Gyrus
8
8
9/46
9/46
2
⫺13
36
⫺49
18
25
34
33
Ventral Prefrontal Regions
R. Inferior Frontal Gyrus
R. Orbitofrontal Gyrus
L. Orbitofrontal Gyrus
47
11
11
51
18
⫺16
42
34
33
Regions of interest
but in that condition we show a decrease in ventral
prefrontal activity. Accordingly, our data appear more
consistent with Cohen and Servan-Schreiber’s [1992]
theory of prefrontal cortex being involved in supporting the representation of relevant task demands.
When response competition is high, ventral prefrontal
cortex helps maintain the relevant response demands
of the task thereby suppressing the representation of
the competing response.
The observed increase in dorsolateral prefrontal cortex with the low target probability presumably reflects
the importance of maintaining relevant stimulus information against interference from competing nontarget stimuli. The dorsolateral activity is consistent
with ERP and fMRI findings reported by McCarthy et
al. [1997] using infrequent targets in a similar oddball
paradigm. In the current study, the percent change in
MR signal for the dorsal areas, although significant,
was modest. The modest increase in signal in more
dorsal prefrontal regions may be due to the lack of
demand in maintaining stimulus information on-line
since the only stimulus of real significance was “X”.
Other studies [e.g., Braver et al., 1997] have shown
that the percent change in MR signal in dorsolateral
prefrontal cortex increases monotonically as a function
of increasing memory load from one to four items. In
our current study, we have the equivalent of a memory load of one item in terms of the relevant item of
information (X), but the target set actually includes 25
letters (all letters of the alphabet except X).
The decrease in dorsal prefrontal activity and increase in ventral prefrontal activity is suggestive of
competing cognitive processes. A similar inverse relationship has been reported in at least one other neuroimaging study to date using positron emission tomography [Drevets and Raichle, 1998]. In that study
the authors report decreases in areas implicated in
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Mean %
difference
Maximum
F-value
Cluster size
in voxels
54
44
26
34
⫺.26
⫺.22
⫺.22
⫺.20
15.40
5.80
8.36
5.31
5
4
8
9
⫺8
⫺7
⫺13
.64
.98
1.22
15.41
15.40
25.68
8
7
3
emotional processing (e.g., amygdala and orbitofrontal cortex) during demanding cognitive tasks and conversely, decreases in areas implicated in cognitive processing (e.g., dorsolateral prefrontal cortex) during
emotion related tasks. Given the similarities of the
prefrontal regions activated in that study relative to
the current one, one may argue that the ventral prefrontal activity observed in the current study reflects
an emotional component to the task. Clearly, the word
“no” has social relevance and so it might seem plausible that an emotional response would occur when an
individual did something they were asked not to do
(i.e., do not respond to X) as in the case of making a
false alarm. However, in the current study, there was
no significant difference in the number of false alarms
during conditions of rare (93% accuracy) and frequent
targets (96%) and so this interpretation appears insufficient to explain the inverse relation between dorsal
and ventral prefrontal activity. Alternatively, these
data may be suggestive of competing cognitive processes or bottlenecks in the processing stream of cognition associated with capacity limitations.
A limitation of the current study is the confound
introduced by the use of a blocked design over an
event-related design. As such, each condition includes
blocks of trials with targets and nontargets and thus
any change observed in the MR signal intensity cannot
be linked to a specific event (no-go vs. go trials).
Accordingly, our interpretation of increases and decreases in ventral and dorsal prefrontal regions, respectively, are confounded. Clearly, event-related
fMRI studies that take into consideration the previous
context of trials (e.g., number of targets preceding a
nontarget or vice versa) will more adequately address
how regions of the prefrontal cortex respond differentially to changes in target probabilities.
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Casey et al. 䉬
The purpose of this study was to determine if regions of prefrontal cortex respond differentially to
changes in target probabilities. We showed that even
within the same task, the manipulation of target probability changes the neural systems involved in performing the task. Any theory of prefrontal cortex function must therefore account for the sensitivity of the
prefrontal cortex to changes in target probability.
Clearly the notion that prefrontal regions are specialized according to spatial vs. object working memory
[Goldman, 1994] or for complex information manipulation versus simple maintenance and retrieval [Petrides, 1994; Owen, 1997] do not adequately address
the findings of the current study. The same type of
stimuli (letters) and task instructions were provided
regardless of whether the target was rare or frequent
and task difficulty was not different between these
conditions. What appeared to change over the course
of the study was the type of interference (i.e., the
stimulus or response demands of the task). Thus our
results appear most consistent with a role of the prefrontal cortex in supporting task-relevant information
from interference similar to that proposed by Cohen
and Servan-Schrieber [1992]. Specifically, the dorsolateral prefrontal cortex appears more involved when
there is interference in stimulus demands, while the
ventral prefrontal cortex appears more involved when
there is interference in the response demands of the
task. However, our results are not conclusive as an
event related fMRI study would more directly link
changes in MR signal to a specific type of event (target
versus nontarget).
mechanisms underlying bottlenecks in the processing
stream of cognition or capacity limitations.
ACKNOWLEDGMENTS
This work was supported in part by a NIMH K01
award to the first author. The authors thank Drs.
Michael Posner, Jonathan Cohen, Bruce McCandliss,
and an anonymous reviewer for their helpful comments on an earlier draft of this manuscript and Clayton Eccard for his help in preparation of this manuscript.
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In sum, we show two interesting results with the
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task. Dorsal prefrontal activity increases as a function
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prefrontal cortex to changes in target probability. Further, we showed an inverse relation between activity
in the dorsal and ventral prefrontal regions with decreases in one corresponding to increases in the other.
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